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Deep Multispectral Semantic Scene Understanding of Forested Environments Using Multimodal Fusion

  • Abhinav Valada
  • Gabriel L. Oliveira
  • Thomas Brox
  • Wolfram Burgard
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 1)

Abstract

Semantic scene understanding of unstructured environments is a highly challenging task for robots operating in the real world. Deep Convolutional Neural Network architectures define the state of the art in various segmentation tasks. So far, researchers have focused on segmentation with RGB data. In this paper, we study the use of multispectral and multimodal images for semantic segmentation and develop fusion architectures that learn from RGB, Near-InfraRed channels, and depth data. We introduce a first-of-its-kind multispectral segmentation benchmark that contains 15, 000 images and 366 pixel-wise ground truth annotations of unstructured forest environments. We identify new data augmentation strategies that enable training of very deep models using relatively small datasets. We show that our UpNet architecture exceeds the state of the art both qualitatively and quantitatively on our benchmark. In addition, we present experimental results for segmentation under challenging real-world conditions. Benchmark and demo are publicly available at http://deepscene.cs.uni-freiburg.de.

Keywords

Semantic segmentation Convolutional neural networks Scene understanding Multimodal perception 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Abhinav Valada
    • 1
  • Gabriel L. Oliveira
    • 1
  • Thomas Brox
    • 1
  • Wolfram Burgard
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburg Im BreisgauGermany

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